Volume 5, Issue 9, September 2015 ISSN: 2277 128X...

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Volume 5, Issue 9, September 2015 ISSN: 2277 128X

International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com

Application of Remote Sensing and Image Processing for Efficient

Urban Planning: A Case Study of Indian City Bhopal Sanjeev Sharma

Assistant Professor,

Dept. of Electronics and Communication Engineering

R. D. Engineering College, Ghaziabad (U.P) India

Dr. S. K. Tomar

Associate Professor and Head

Dept. of Electronics and Communication Engineering

IET MJP Rohilkhand University, Bareilly (U.P) India

Abstract--- In India with increasing population there is a greater need for developing an information base for

effective and faster management of urban area. It requires characterizing the urban planning it’s monitoring and

control while considering the environmental condition and cost effectiveness with changing technologies.

The application of remotely sensed data has been found increasing in recent years. They collect a reliable data about

the land-based objects with synoptic view of large area at a time, which is not possible from conventional survey

methods. The digital data requires further interpretation by using digital image processing technique.

In this paper we have used Artificial Intelligence system to detect the roads from Multispectral Remote sensed data.

This data with combination with some other relevant data provides us the key to model urban planning to any country.

Therefore accurate and updated Information on the status and trends of urban resources is needed to develop

strategies, and efficient planning and management of the resources of the city for sustainable development and to

improve the livelihood of cities.

Keywords— Remote Sensing, Urban planning, Multi Spectral, urbanization and Neuro Fuzzy

I. INTRODUCTION

With a land area of 3.2874 million square kilometers and a population of 1.2 billion, India has 2.4 percent of world

land area but supports 18 percent of world population. By 2020, India is estimated to have a population of 1.33 billion,

with an astonishing demographic dividend; average age of an Indian would just be 29, compared to 37 in China and 48

for Japan. With economy poised to grow at 9 percent per annum, gainful employment of the huge workforce is both the

challenge and the opportunity for India. Seventy percent of the Indian population lives in villages while 30 percent of the

population lives in urban habitats. With economic growth, urbanization is projected to expand further, requiring to

support 40 percent of the population by 2020. Most industrial and economic investments would be made in and around

the urban habitats as part of the unabated economic growth.

Although scientists are engaged in using the remote sensing data applications in urban planning, yet the efforts are

not adequate [2].

There are models from India and abroad trying to give proper planning scenario but does not explain complete

prospects for public needs:

Chesko's Midtown Manhattan model: In this model, Skyscrapers grabs attention but real effort of them was in its

details. Approximately 400 individual block were created in the district by tackling block by clock miniaturization.

Michael Cheskospent devoted approximately 2000 hours for construction this midtown Manhatan model having high

level of details and perfection in scale. He used Dremel, nail file and x-acto knife only, to sand, cut and glue the snow of

mini metropolis which is placing in museum of New York Skyscraper.

Bay Model: This model was created by U.S. army engineers‟ corps, which was used for simulation the effect of disaster

and public projects on tides and currents, in 1957. It was made by 286 slabs of a ton weight each made of concrete

representing a total of six bridges with waterworks manipulation by a hydraulic system controlled by computer.

Socio-industrial model: The classic model in India which gave birth to industrial cities such as Bhilai, Bokaro, and

Rourkela (all based on government owned steel plants) as well as Jamshedpur and Jamnagar (based on private sector

steel, vehicle and petrochemical plants) was based on industries acting as engines of development. Despite generation of

huge wealth these cities have failed to reach the status of even tier 2 cities in social attractiveness index. The same may

be said of the several suburban industrial hubs that came up, or are coming up, near established top cities in various

states. Despite major industrial investments, some new industrial cities have not measured up to the desired levels of

sustained factor inflows. At the same time, even though each State has at least ten to twenty urban habitats which could

have supported a larger industrial growth given their factor endowments such towns remained tier 2 habitats. Clearly, the

trigger for synergistic socio-industrial growth is hard to identify.

Connectivity-productivity model: Experience in India and elsewhere suggests that connectivity is an essential element

of industrial productivity. The Indian rail and road systems are saturated. The railway system has remained virtually

static for decades while the national highway and golden quadrilateral road systems merely catch up with the current

congestion. If the Indian railways create a new 10,000 kilometer cross-country rail network, connecting hitherto

http://www.ijarcsse.com/

http://www.downtownexpress.com/de_270/skyscrapermaven.html

Sharma et al., International Journal of Advanced Research in Computer Science and Software Engineering 5(9),

September- 2015, pp. 228-238


unconnected tier 2 and tier 3 cities there would be all-round triggers for industrial dispersal. This when integrated with

dedicated freight corridors and bullet trains would usher in a sea change as to how industries and societies feel connected

with each other. The connectivity model would be a great new way of stand-alone employment besides channeling

industrial investment into the interiors to spur further employment[15,16].

Role models and thought leaders: It is gratifying that India has many public sector and private sector role models that

excelled in creating these systems in their own way. The top 21 public sector undertakings belonging to the Maharatna

and Navaratna categories and the several top private sector conglomerates as well as some individual mega companies

have excelled in creating assets, skills and wealth in barren tracts inIndia. Even as these and other groups venture into

other countries to secure factor and cost advantages, focus must also be on the potential that still remains to be exploited

within India itself to achieve more intense and more diversified social and industrial transformation.

All models from India and abroad gives proper planning scenario but does not explain complete prospects for public

needs. Our model fulfills above all requirements. Needed for proper growth for healthy environments. So migration may

be reduced from small cities to developed cities and nations too.

II. SATELLITES AND SENSORS

Viewing and image targeting may be done by placing instruments related to Remote sensing on different base.

Aircraft base or ground base may be used as satellite provides great opportunity for imaging of remote sensing. A

satellite has various type of characteristic and such type of characteristic is used in Earth surface remote sensing.

Basically orbit may be defined as path which is followed by a satellite. Satellite orbits are matched to the capability and

objective of the sensor(s) they carry. The selection of orbit can be in different terms Altitude, Orientation and Rotation

relative to earth.

So if we take a view at very high altitudes, which view the same portion of the Earth's surface at all times have

geostationary orbits. The rotation of geostationary satellites, at altitudes of approximately 36,000 kilometers matched

with the Earth‟s rotation by which they have stationary position with the earth. According to the rotation, satellites can be

observed and collect information continuously across specific areas. Generally this type of orbit have found in case of

weather and communications satellites. Geostationary satellite related to weather forecasting monitors pattern of cloud

and weather of a particular hemisphere of earth. [17,18].

A. GOALS OF SPACE PROGRAMME IN INDIA

Objectives of Space Program in India are for tackling these technologies for applications in the areas of remote

sensing, search & rescue operations, disaster warning, meteorology, broadcasting and communications. Operational

systems are applied in the above mentioned areas from nearly last twenty years. Elements of remote sensing related to

program, particularly this way, achieved world-wide acceptance. We have built operational satellites and launched and

they are performing well in several application like ocean and land.

B. INDIAN SATELLITES FOR REMOTE SENSING

Following are the milestones for Indian remote sensing abilities.

Bhaskara (1 & 2): These were trial satellite for remote sensing. Bhaskara 1 was launched in June 1979 and Bhaskara 2

in Nov 1981. We achieve experience for further developments in Space Research Program of India.

IRS (1A & 1B): The first generation, operational remote sensing satellite IRS-1A and IRS-1B were launched in

march1988 and august 1991 respectively. These satellites carried one Linear Imaging sensor (LISS-I) and one Self

Scanning sensor (LISS-II) which can provide data having resolution 72.5 and 36.25 meters containing four spectral band

and having 22 days receptivity. They provides vital data during the period of operations for projects of national

importance.

IRS - P2: Polar Satellite Launch Vehicle „PSLV-D2‟ was used for launching of IRS-P2 satellite in October 1994. It

contained modified version of LISS camera.

IRS-1C & IRS-1D: The next generation operational remote sensing satellite IRS-1C & IRS-1D are launched at last of

20th

century (Dec. 1995 & Sept. 1997) having improved sensor and coverage characteristics.

IRS - P3: PSLV – D3 launch vehicle was used for launching it in Apr. 1996. This satellite‟s payload includes three

sensors, two imaging type and one non imaging type sensors. One of the sensor named WiFS (Wide Field Sensor) is used

to send data containing three spectral band having 188m of spatial resolution in the regions of near infra red and visible

containing 810 Km of swath. Remaining two sensors used are MOS (Modular Opto-electronic Scanner) & payload

related to astronomy of X-ray. Products of MOS & WiFS data are disseminated for users.

OCEANSAT-1 or IRS-P4: It was launched in May 1999 and is eighth in the Indian Remote sensing Satellite program.

One OCM (Ocean Color Monitor) is included in its payload which may operate in eight spectrum consisting infra red and

visible region and MSMR (Multi frequency Scanning Microwave Radiometer) which can operate in four frequencies

(6.6, 10.6, 18.0 & 21.0) GHz. The above mentioned sensors are used for biological and physical parameter measurements

in ocean.

CARTOSAT-1 or IRS-P5: It has capability of fore-aft stereo and two PAN sensors having resolution of 2.5m. Its

payload has capabilities of applications like- cadastral mapping, terrain modeling and cartography etc.

OCEANSAT-2: The mission of this created to offer extension in the services of Oceansat-1 for its users. It has advanced

capabilities. It consist an Ocean Color Monitor (OCM) and Wind Scatter meter. The application of it includes snow

mapping, ship routing, Flood inundation mapping, paddy crop yield estimation and acreage, Monitoring etc.


September- 2015, pp. 228-238


C. RESOURCESET-1 (IRS P6) AND ITS SENSORS

IRS-P6 mission have two main purposes:

(a) It provides services related to data of remote sensing for water resources and integrated land management with

upgraded spatial coverage and multi spectral containing stereo imaging capability at micro level.

(b) It is used for developing study in the areas of advance level like- urban management, disaster management,

pest/disease surveillance, crop stress, crop yield, crop discrimination etc.

SPECIFICATION: IRS - P6 is a spacecraft having body stabilization in three axes, which was launched by PSLV – C5 at

altitude of 817 Km in an orbit of sun synchronous, descending node and repetvity 341 orbits / cycle (24 days). The

spacecraft has planned with 5 years of expecting life. Its payload contains a total of three optical camera.

Sensors of RESOURCESET-1 (IRS p6)

(1) LISS-IV - LINEAR IMAGING SELF SCANNING SENSOR CAMERA

LISS-IV has a high-resolution multi-spectral camera operating in three spectral bands

a) 0.52 to 0.59 m.(Green (band 2).

b) 0.62 to 0.68 m (Red (Band 3).

c) 0.76 to 0.86 m (NIR (Band 4).

LISS-IV: It may be operated in multi spectral mode „Mx‟ as well as mono mode, with a 5.8 m resolution on ground. A

swath covering 23.9 Km, out of 70 Km, may be achieved in multi spectral mode „Mx‟ and full swath covering 70 Km

may be achieved using mono mode „Mono‟ using any single band which may be select by ground most probably red

band-B3. The camera of LISS – IV provides 5 days revisit period and may be tilted across track direction upto ± 26°.

(2) LISS-III - LINEAR IMAGING SELF SCANNING SENSOR CAMERA

This camera has 141 Km of swath containing all four bands. It has 23.5 m of spatial resolution of SWIR band

„B5‟ and rests of the features are same as in aircraft containing LISS – III flown in IRS – 1C / 1D.

(3) AWIFS - ADVANCED WIDE FIELD SENSOR

This camera is the upgraded form of the WIFS camera which is flown in IRS – 1C / 1D. It may provide 740 Km

of swath and 56 m of spatial resolution. For wide swath covering, the ASIFS camera is spited into two separate modules

of related to electro optics namely ASIFS – A and ASIFS – B. For enhancement the capabilities for supporting the

operations related to Payload, spacecraft mainframe „IRS – P6‟ is configured in respect of some new features.

(Note: we calculate one pixel in kilometers.)

Linear Imaging Self-Scanning Sensor – 4

i) Contains VIS / NIR radiometer of 3 channels, each channel contains a camera.

ii) Contains Bush broom having 4096 pixels per line per camera having 23.9 km of swath for using three camera, one

camera for one channel „multi-spectral‟, and a swath of 70 km for using all camera in the case of same channel parallel

strip viewing „panchromatic‟. It has a capability of ±26° related to pointing of cross track for orbits stereoscopy.

iii) Then for one channel=23.9 km.

iv) Value of one pixel (in km) = (23.9*23.9) / (512*512) km² = 0.0022 km².

v) It covers full globe in 24 days and covers any target area in five days with a use of cross track pointing.

III. MATERIALS AND METHODS

A. STUDY AREA: BHOPAL

Fig 1. Map of Bhopal district (Madhya Pradesh)


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Focus of our study is on areas in the city of Bhopal, which belongs to north central state of Madhya Pradesh. It

has a population of almost 1.2 million people. The position of Bhopal in the centre of india having the height from sea

level of 1637 feet (499 m). It is situated on Malwa plateau hence having higher level to the northern plains of india. The

northern side of Bhopal is covered by three hills viz- Idgah hills, Shyamala hill and central Arera hills. Bhopal contains a

beautiful lake which is known as Bhoj Watland built by King Bhoj, which is divided into two parts viz- Bada Talab (area

361 km²) & Chota Talab (area 9.6 km²). Upper Lake contains a national park known as Van Vihar National Park. The

climate of Bhopal is humid.

B. CLIMATE & GEOGRAPHY OF BHOPAL

The climate of Bhopal is subtropical and humid having mild and dry winter and hot & long summer, monsoon

season having humidity. The duration of summer season is between last-March and mid-June, having average

temperature 30°C with a highest temperature of more than 40°C in May. The duration of Monsoon season is between

last-June and last-September. In Monsoon season, flooding, thunderstorm and 100cm precipitation (with 115cm annual

rainfall) takes place, with 25°C of average temperature and very high humidity. Bhopal has dry, sunny and mild winter

with 18°C average temperature. In January minimum temperature nearly freezing point may occur.

C. METHODS OF ANALYSIS

To analyze of the multi spectral image, we have employed different Image Processing algorithms like Artificial

Neural Networks (ANN) and fuzzy c-mean clustering (FCM) to generate the False Color Composite of the multi spectral

image or to emphasize some of the features of the image that may be the particular regions or pixels of the image.

For the analysis of the multi spectral images using ANN, what we have used is the Back propagation algorithm.

In this algorithm, we at first define a neural network which reflects the mapping relationship between the input data and

the target. The input data are the pixels which best represent the particular feature of the image, may the vegetations, the

water bodies or the concrete structures; and the target is the desired values of the colors for the FCC of the image. After

the learning of the network, it is applied to whole the image to generate the desired FCC. After generating FCC image we

worked for FCM image and got the values accordingly. For getting the results due to NEURO FUZZY we took the FCC

image of ANN as the data and applied it for FCM.

But in the remote sensing situations are more complex, as energy response of the object may not be received

correctly because atmosphere can absorb the emitted energy, problem could be because geometric and radiometric

distortions. So we are required the certain corrections.

Energy response of the objects may not be received correctly because atmosphere can absorb the emitted

energy, so on the basis of the ground survey we can minimize the effect of the atmospheric effects. As in our study we

are using the FCM values also for classification of the objects, we have found the threshold values of FCM in sample

locations for each object in a particular climate conditions. And after this we have generalized the values for whole

image.

D. ANALYSIS OF THE MULTI SPECTRAL IMAGE USING ANN

Step 1 Assembling the Training Data

We have received the image of the Bareilly Ramganga region as shown in the figure 5.2 and by using the Data Cursor

tool in the MATLAB we have obtained the R-G-B components of the pixels which best represent the different features

of the image like the River & Water Bodies, the Concrete Structures, the Roads and the Vegetation and created the table:

Table 1 Few of Pixels representing different Features

Features R G B

River &

Water

Bodies

120 059 056

128 049 052

131 051 060

136 081 076

142 052 058

Concrete

Structures

151 104 096

172 093 088

181 059 074

186 074 088

197 098 104

Roads

154 049 064

163 052 068

169 096 087

172 074 089

178 062 073

224 086 101


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Vegetations 229 119 130

230 096 105

234 082 095

240 104 114

Fig 2.Original image of Bhopal

Thus we obtained R-G-B values of almost 100 pixels and these values may be moulded to form a 3x100 matrix as in

figure

Fig 3. The Matrix of Input Pixels

Step 2 Create the Network Object

Now we define the network and specify its features like no. of neurons, rangee of the values of the input

neurons, no. of layers etc. and specify the input and target matrices. In target matrix, there is a particular color for the

particular feature to generate the FCC

Fig 4. The Matrix of Target FCC

Fig 5. Training of network

Now starts the training of the network and the weights are assigned automatically. Since, the input and output

are already defined; the weights for each input-output pair can be developed. Thus, the step 2 makes it to meet the

requirement of the mapping relationship between the input and the target.

Step 3 Simulate the Network Response for Whole the Image

Now, as we have obtained the function representing the relation between the input and the target, we are ready

to generate a resulting matrix corresponding to the final FCC of the given image.


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But, before we simulate the image with the help of given network of neurons, we are to convert the 3-

dimensional matrix of dimensions „512 x 512 x 3‟ corresponding to the multi spectral image into a 3-dimensional matrix

of dimensions „3 x 512x512‟ i.e. „3 x 262144‟ .

Now this converted form of the multi spectral image applied to neural network for the simulation. After the

simulation, what we get as the result is again a 2-dimensional matrix of the same dimensions „3 x 26212144‟, which is

again to be converted into the 3-dimensional matrix of dimensions which represents the FCC corresponding to the given

multi spectral image.

Fig 6. FCC Image of the Multi Spectral image

E. ANALYSIS OF THE MULTI SPECTRAL IMAGE USING FCM

Step 1: In the very first step we receive the multispectral image and convert it into the double image to apply the FCM

algorithm on the image.

Step 2: After we have got the double image, we apply the nir,red and green band images algorithm of the FCM on the

multi spectral image and analyze the averaged component of this multispecal image

Step 3: Now as we have obtained the arrange data in a matrix component of the multi spectral image, using “reshape”

function.

Step 4: After getting the images of the different bands and reshape the image now our aim is to find out the cluster

values of FCM of the given image

[CTR,CLASS]=FCM(DATA,4);

By using the MATLAB we got the cluster values of each pixel and finally we got the image. The values of cluster of

FCM are in the range of 0 to +1 [10]

Step 5: Now we calculate the reshape of class data using reshape function.

Step 6: Next aim after getting the FCM image was to get the classified images of the region, in order to get the these

images we performed the ground survey at various places of the city and taken the proper longitude and latitude values of

the survey region from Google earth, and with the help of that we identified the survey locations into the multi spectral

images. By the help of the ground survey I found the specific range of the FCM for each object.

1. For the vegetation, I found the FCM vales are in between 0 & 1, the concentration of the vegetation is in the

range of the 0.01 to 0.1:

(fcm(m,n)<0.1) & (fcm(m,n)>=0.01)

2. For the dense structures, I found the FCM vales are in the range of between 0.1 to 0.2:

(fcm(m,n)<=0.2) & (fcm(m,n)>=0.1)

3 .For the roads, I found the FCM vales are in the range of between 1 & 0:

(fcm(m,n)<1.0) & (fcm(m,n)>=0.5)|((nir(m,n)<0.79) & (nir(m,n)>0.7))

4. For the water, I found the FCM vales are in the range of between 1 & 0:

(fcm(m,n)<0.225) & (fcm(m,n)>0.2)

5. For the weak structure, I found the FCM vales are in the range of between 1 & 0:

(fcm(m,n)<0.5) & (fcm(m,n)>=0.225)

6. For the land, I found the FCM vales are in the range of between 1 & 0:

(fcm(m,n)<0.01)

IV. RESULTS FOR IMAGES AFTER APPLYING DIFFERENT ALGORITHMS


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Fig 7. Images of Bhopal using different algorithms

V. ACCURACY OF RESULTS

Here is a table giving an approximate analysis of all these two algorithms and their results:

Table 2 .Table representing accuracy of results for Bhopal

AREA COVERED FCM % RESULT NEURO FUZZY % RESULT

TOTAL AREA 388.0800 100 388.0800 100

VEGETATION 261.4612 67.3730 317.9528 81.9297

WATER BODIES 70.6794 18.2126 115.1018 29.6593

ROADS 113.3572 29.2098 83.4438 21.5017

DENSE STRUCTURES 226.9586 58.4824 138.5560 35.7029

WEAK STRUCTURES 52.2830 13.4722 25.3726 6.5380

FREE LAND 6.9982 1.8033 84.2996 21.7222

Fig 8 Graph Showing the comparative percentage between FCM and Neuro- Fuzzy

VI. EDGE DETECTION

Edge correspond to large changes in intensity of neighbors pixels in at least one direction. Edge is defined as

that place on which contrast with a strong intensity takes place in an image. It may also be defined the area where a color

difference occurs without a change in intensity. Edge defines the boundaries between regions in an image, which helps

with segmentation and object recognition. Edge detection is a fundamental of low-level image processing and good edges

arenecessary for higher level processing. In general edge detectors have difficulty adapting to different situations. The

quality of edge detection is highly dependent on lighting conditions, the presence of objects of similar intensity, color etc

[10].

The task of edge detection may be achieved using several methods. All the methods may be divided into two groups:

There are many ways to perform edge detection. However, the majority of different methods

May be grouped into two categories:

i) Gradient: In this group of methods edge is detected using maxima and minima in first derivative of image.

ii) Laplacian: Edge is detected in this method by searching zero crossing in second derivative of inage. One dimensional shape

of ramp is known as edge and its location may be highlighted by calculating the derivative of image [10, 12, 13].

A. DIFFERENT EDGE DETECTORS

1) Sobel Edge detector

Sobel Edge detector is used to compute the approximation of gradient of function of image intensity and is an operator to

calculate discrete differentiation. Sobel operator represents norm of the vector or corresponding gradient vector at each

point of image. Sobel operator performs fewer computations as it is based upon the computation of convolution of image


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with separable, small and integer value filter in vertical and horizontal direction. This operator provides highest

increment from light to dark as well as rate of change of direction by calculating gradient of image intensity on each

point. The edge may be detected using this technique as the results shows „abrupt‟ or „smooth‟ change at a point in image

[14].

Sobel edge detector creates series of magnitudes of gradient by using a simple type of kernel related to convolution. This

may be mathematically represented by applying K (convolution) to p (pixel group) as follows:

N(x,y) = .

Hence sobel edge detector is using two kernels related to convolution first one for detection horizontal contrast (hy) and

another for detection for changes in vertical contrast (hx).

(hx)=. , (hy) =

(Sobel edge detector mask)

x-component and y-component of this vector represents two different gradients representing (hx) and (hy).

g = , g = , ɵ =

Where g is the gradient vector, g is the gradient magnitude and θ is the gradient direction.

2) Prewitt Edge Detector

Prewitt operator is similar to the Sobel operator and is used for detecting vertical and horizontal

edges in images

(hx) , (hy)=

Prewitt edge detector mask. Robert‟s Cross Detector

This detector is used to measure 2D spatial gradient of an image in simple and quick manner. Absolute

magnitude of spatial gradient of a point in input image is represented by the pixel value of that point in the output image.

The Roberts Cross operator performs a simple, quick to compute, 2 D spatial gradient measurement on an

image. Pixel values at each point in the output represent the absolute magnitude of the spatial gradient of the input image

at that point.

The Roberts Cross operator included (2 by 2) convolution kernels where other kernel may be obtained by rorating the

first one by 900.

Robert edge detector mask.

The above two kernel responds maximally for the edges which are situated at an angle of 45° in the pixel grid and one

kernel is needed for two orientations at perpendicular directions to each other. These two kernels may be applied on input

image, separately, which provides different measurements for each orientation for the gradient component „Gx‟ and „Gy‟.

The magnitude of the gradient is as follows:

The approximate value of the magnitude may be given as:

|G| = |Gx|+|Gy|

This computation may be done much fast.

Orientation angle related to edge, which express the increment in the spatial gradient which is related to grid orientation

of pixel, may be expressed as:

ɵ = arctan(Gy/Gx) – 3x/4

B. LAPLACIAN OF GAUSSIAN : If we calculate second derivative (spatial) of image and then calculate 2D measure (isotropic) of it, then the result is

Laplacian. Laplatian is generally used for detection of edge as it highlights the regions where the change in intensity is

rapid in an image. Firstly the sensitivity for noise of the image is reduced using Gaussian filter for smoothing then

Laplacian is applied. The input and output to this operator are normally image of single gray level. The value of

Laplacian of an image L(X, Y) of the image having pixel intensity I(X,Y) is denoted by:

L(X,Y) =

Now we will find kernel with discrete convolution which may be approximated 2nd

derivatives in Laplacian, due to input

image containes discrete pixel set.

Below diagram represents three kernels which are commonly used:


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0 1 0 1 1 1 -1 2 -1

1 -4 1 1 -8 1 2 4 2

0 1 0 1 1 1 -1 2 -1

(Laplacian edge detector mask)

The above kernels are very sensitive for noise as they approximate the measurement of second derivative in

image. Hence before applying Laplacian, smoothing is done upon the image through Gaussian operator. It is known as

preprocessing which reduces high frequency components of noise before the next step of differentiation.

We can use the convolution after the result of Gaussian Filter for smoothing with Laplacian filter, finally applying

convolution upon this combined filter on image, due to convolution operator follows the associative law, and hence final

result is achieved.

Fig 9. Showing different edge detector techniques

As in this case of edge detection it is not possible to detect the spies of multispectral Images. It may be possible to detect

in case of RGB images.

VII. PROPOSED METHODOLOGY TO DESIGN MODEL

In the proposed researched attempt is made to overcome the above deficiencies by evolving a systematic methodology

explained.

(1) Select a region of interest and collect the satellite image for this region. The land-based objects will be identified

using image-processing technique [19]. Image cluster of similar pixels characteristics can be formed by Edge

detection technique algorithm in the following steps.

(2) Having formed the image clusters in above steps, the area of the urban land will be estimated. The road & road

junctions can be identified which will help in estimating the urban yield of the region of interest. This step will

require use of fuzzy-neural technique applied to image processing. This technique can also be used for

identifying the defects in urban land.

(3) A simulation model of urban area will be developed with inputs like land area, material supply facility,

atmospheric conditions, road & road junctions. The model would estimate the construction quality and

compares it with target and feedback the error to the model input. This works in an iterative manner till a

converged solution is obtained.

Fig10. Proposed Urban planning model


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VIII. PROPOSED LOOK OF BHOPAL REGION FOR PLANNING FOR URBANIZATION

Fig 11. Showing the boundaries of Bhopal Region

In above figure the middle part shows the dense population region of Bhopal which cannot be further urbanized

due to its old plan policies. The only part which is shown by white colour is nonagricultural part which can be planed and

urbanized according to developed cities. Taking the considerations of junctions of National High ways and State

Highways for planning of towns, hospitals, schools, Institutes, Industries and Malls etc.

IX. CONCLUSION

If we examine the results obtained from the three algorithms applied on the multispectral image, it is found that

there are different pixels obtained by different algorithms. The ANN method has all the very good results for all the six

features presented here in the multispectral image and almost all the pixels are trained in this case.

The FCM algorithm has better result in most of the cases than ANN. We have worked here on NEURO FUZZY

method and can conclude here that it is not giving as better result as expected because it is possible that we have not

trained the pixels upto that much level as needed by NEURO FUZZY to give the best results. If we train the pixels in a

very efficient way then it must be possible to get the more accurate results as expected. In case of edge detection no

relevant results were obtained so that those results may be used for further planning.

The model developed above will provide the following advantages. (i) Easy data acquisition (ii)Reduce the amount of

survey work. (iii) Efficient monitoring and control of urban planning system (iv) Develop an efficient decision support

system for urban area.

By the help of urban planning model it is possible to obtain the development cities which may fulfill many

needs of human being and provide no reason to migrate to developed cities. All these classification errors can be reduced

by using the more higher resolution devices and hyper spectral images from the satellites, such satellites may be launched

by the Indian government in coming years.

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[18] Hand book of IRS P-6Resouresat-1 and its sensors

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